Machine-learning based multi-step engagement strategy generation and visualization

ABSTRACT

Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users). Once the multi-step engagement strategies are generated, the learning-based engagement system may also generate visualizations of the strategies that can be output for display.

RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/057,729, filed Aug. 7, 2018, which isincorporated by reference herein in its entirety.

BACKGROUND

Digital advertising systems continue to make advances in computingtechnologies to target digital advertising to consumers. With theseadvances, digital advertising systems are able to increasinglypersonalize digital advertising, such that consumers served digitaladvertisements have begun to think and/or say “I do want this [productor service that is the focus of the served digital advertising], but howdo ‘they’ know?” Regardless of whether digital advertising systems arecapable of accurately predicting products and services desired by clientdevice users and serving them digital advertising for those products andservices, digital advertising systems also attempt to optimize (e.g.,maximize) conversion of these client device users in relation to theproducts and services. Generally speaking, “conversion” refers to an actof consumers to follow through with a desired action, such as topurchase a product or service or engage with a desired experience.Examples of conversion include interaction of a consumer to engage withthe digital content (e.g., click a link), purchase a product or servicethat pertains to the digital content, and so forth.

Conventional digital advertising systems attempt to optimize conversionin different ways, such as by configuring communications with differentdigital content components (e.g., images, videos) for different usersegments, predicting times to send communications (e.g., emails) toclient device users, predicting user fatigue, and so forth. However,conventional digital advertising systems rely heavily on the involvementof users (e.g., marketers) for creating strategies to deliver content toclient device users targeted by marketing campaigns, such as inconnection with creating strategies for delivering content to particulargroups of client device users over a life of a campaign. Human userssimply are incapable of processing the vast amount of data availablewhich describes client device users and their interactions withdeliveries of previous campaigns. By relying on the involvement of humanusers, conventional digital advertising systems thus deliver contentaccording to strategies that are based on information that is, at best,incomplete, but may also be biased or simply inaccurate, e.g., becauseit is practically, if not actually, impossible for a human to processthe amount of data that is pertinent to delivering content to targetedclient device users.

SUMMARY

To overcome these problems, multi-step engagement strategy generationand visualization is leveraged in a digital medium environment. Ratherthan rely heavily on human involvement to create delivery strategies,the described learning-based engagement system generates multi-stepengagement strategies by leveraging machine-learning models trainedusing data describing historical user interactions with contentdelivered in connection with historical campaigns. By contrast, usersthat create such strategies on their own may merely provide their “bestguess” based on domain knowledge as to the aspects of the sequence thatwill optimize conversion. Importantly, the learning-based engagementsystem is able to leverage and make determinations based on an amount ofdata that is practically, if not actually, impossible for a human toprocess. Initially, the learning-based engagement system obtains datadescribing an entry condition and an exit condition for a campaign.Based on the entry and exit condition, the learning-based engagementsystem utilizes the machine-learning models to generate a multi-stepengagement strategy, which describes a sequence of content deliveriesthat are to be served to a particular client device user (or segment ofclient device users). These machine-learning models are also trained togenerate predictions of multi-step engagement strategies that optimizeperformance of an action corresponding to the exit condition acrossclient device users targeted by the campaign. Once the multi-stepengagement strategies are generated, the learning-based engagementsystem may also generate visualizations of the strategies that can beoutput for display, e.g., to a marketer that provided input specifyingthe entry and exit conditions.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures.

FIG. 1 is an illustration of an environment in an example implementationthat is operable to employ techniques described herein.

FIG. 2 depicts an example implementation of data describing a multi-stepengagement strategy for delivering content of a campaign.

FIG. 3 depicts an example user interface via which a user provides inputfor entry and exit conditions that are used by the learning-basedengagement system to generate and present a multi-step engagementstrategy.

FIG. 4 depicts an example user interface via which a user can create amulti-step engagement strategy and select to enable a learning-basedengagement system to generate a modified multi-step engagement strategyusing machine learning.

FIG. 5 depicts an example implementation in which the learning-basedengagement system of FIG. 1 generates a modified multi-step engagementstrategy using machine learning from an input multi-step engagementstrategy.

FIG. 6 depicts a procedure in an example implementation in which amulti-step engagement strategy is visualized for display via a userinterface.

FIG. 7 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilized with reference to FIGS. 1-6 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION Overview

Digital advertising systems continue to make advances in computingtechnologies to target digital advertising to consumers. With theseadvances, digital advertising systems are able to increasinglypersonalize digital advertising, in part, by predicting products andservices desired by client device users and serving them digitaladvertising for those products and services. Digital advertising systemsalso attempt to optimize (e.g., maximize) conversion of these clientdevice users in relation to the products and services, such as to causethese users to purchase a product or service. Conventional digitaladvertising systems attempt to optimize conversion in different ways,such as by configuring communications with different digital contentcomponents (e.g., images, videos) for different user segments,predicting times to send communications (e.g., emails) to client deviceusers, predicting user fatigue, and so forth. However, conventionaldigital advertising systems rely heavily on the involvement of users(e.g., marketers) for creating strategies to deliver content to clientdevice users targeted by marketing campaigns. By relying on theinvolvement of human users, conventional digital advertising systemsthus deliver content according to strategies that are based oninformation that is, at best, incomplete, but may also be biased orsimply inaccurate, e.g., because it is practically, if not actually,impossible for a human to process the amount of data that is pertinentto delivering content to targeted client device users.

To overcome these problems, multi-step engagement strategy generationand visualization is leveraged in a digital medium environment. Ratherthan rely heavily on human involvement to create delivery strategies,the described learning-based engagement system generates multi-stepengagement strategies by leveraging machine-learning models trainedusing data describing historical user interactions with contentdelivered in connection with historical campaigns. By contrast, usersthat create such strategies on their own may merely provide their “bestguess” based on domain knowledge as to the aspects of the sequence thatwill optimize conversion. Importantly, the learning-based engagementsystem is able to leverage and make determinations based on an amount ofdata that is practically, if not actually, impossible for a human toprocess. Initially, the learning-based engagement system obtains datadescribing an entry condition and an exit condition for a campaign.Based on the entry and exit condition, the learning-based engagementsystem utilizes the machine-learning models to generate a multi-stepengagement strategy.

Broadly speaking, a multi-step engagement strategy controls contentdelivery in connection with a campaign, such as by controlling theclient device users that are targeted for receipt of content associatedwith the campaign, sequences of content deliveries that are served toeach of the targeted client device users, actions that result in thetargeted client device users no longer being targeted by the campaign,and so forth. To initiate creation of this new multi-step engagementstrategy, an engagement-system user may be relied upon merely forproviding input via a user interface associated with the learning-basedengagement system to define the entry and exit conditions. Thiscontrasts with conventional systems which rely on users to providefurther strategy information, which may be based on their limited domainknowledge, such as how many content deliveries to make to differentusers, times at which to deliver content, and so forth. Instead, thelearning-based engagement system computes sequences of contentdeliveries that are to be served to targeted client device users usingsimply the entry and exit conditions input by the engagement-system userand also by leveraging the machine learning model.

The entry condition corresponds to an action, that when performed by aclient device user, causes the user to become a target for receivingdigital content in connection with a respective campaign. Examples ofactions that may be leveraged as an entry condition include purchasing aticket having a level that is less than a “best” level (e.g., an economyticket when first-class tickets are still available or general admissionwhen premium seating is still available), leaving a brick-and-mortarstore without purchasing a product or service, leaving an online storewithout purchasing a product or service, adding a product or service toan online shopping cart without purchasing the added product or service,and so on.

In contrast, the exit condition corresponds to an action, that whenperformed by a client device user targeted by the campaign, causes theuser to no longer be a target for receiving digital content inconnection with the campaign. In other words, the exit conditioncorresponds to an objective, such that when the exit-condition action isperformed the learning-based engagement system has effectively causedconversion of the respective client device user in relation to theobjective. Examples of actions that may be leveraged as an exitcondition include upgrading from a lower ticket level to a higher ticketlevel (e.g., upgrading to a first-class ticket or premium seating),returning to a brick-and-mortar store to purchase a product or service,purchasing a product or service from an online store (e.g., associatedwith brick-and-mortar locations and/or limited to online sales),purchasing a product or service previously added to an online shoppingcart but not previously purchased, and so forth.

The sequences of content deliveries each correspond to multipledeliveries of content, which are scheduled for delivery in a definedorder. In scenarios where a user creates a multi-step engagementstrategy, the creating engagement-system user must provide input via theuser interface to define various aspects of these sequences, such as aparticular client device user or segment of client device users to whichthe sequence is targeted, a number of content deliveries of thesequence, the content components included in different deliveries, anorder in which the different deliveries are served, days and times forserving the different deliveries, delivery channels (e.g., email, SMStext message, or social networking platform) via which the differentdeliveries are served, and so on. As noted above, engagement-systemusers may be able to provide their “best guess” based on domainknowledge as to the aspects of a sequence that will optimize conversion.However, the learning-based engagement system relieves users of thisburden by determining different sequences that vary in numerous aspectsfor achieving optimal performance of the exit condition.

In contrast to a “best guess,” the learning-based engagement systemleverages machine learning models that are trained using data describinghistorical user interactions with content delivered in connection withhistorical campaigns, e.g., data describing actual behaviors of userswith real content deliveries. These machine-learning models are alsotrained to generate predictions of multi-step engagement strategies thatoptimize performance of an action corresponding to the exit condition byclient device users targeted by the campaign. Once the multi-stepengagement strategies are produced, the learning-based engagement systemgenerates visualizations of the strategies that can be output fordisplay, e.g., to a marketer that provided input specifying the entryand exit conditions.

Additionally, the learning-based engagement system is capable ofmodifying a multi-step engagement strategy as it is deployed usingmachine learning. Based on tracked interactions of users with contentdelivered in connection with the campaign, for instance, thelearning-based engagement system is capable of optimizing performance ofthe exit condition among targeted client device users given real-timeinteractions with the delivered content. By way of example, thelearning-based engagement system modifies aspects of a content-deliverysequence such as a number of deliveries to a particular client deviceuser, whether or not to serve content to this user, an order ofdelivering different content, content components included in thedifferent deliveries, time of day to serve the deliveries, channels viawhich to serve the deliveries, and so on.

Due at least in part to leveraging the vast amount of data thatdescribes users and their interactions with actual content deliveries,the learning-based engagement system can generate engagement-strategiesin real-time that are effective to optimize performance of an exitcondition based on actual user behavior rather than on incomplete domainknowledge. When such a multi-step engagement strategy is deployed, itsmultiple content deliveries personalize a journey of a client deviceuser through a campaign from end-to-end, e.g., from entry into thecampaign by satisfying the entry condition, during the campaign whilereceiving a personalized sequence of communications, and until exit fromthe campaign by satisfying the exit condition. Due to thepersonalization of this journey, client device users that receivecontent deliveries according to multi-step engagement strategies developless fatigue with a respective organization.

Term Descriptions

As used herein, the term “multi-step engagement strategy” refers to datathat defines a sequence of different content deliveries to a singleclient device user or a segment of client device users, such that asingle client device user is delivered multiple content deliveriesaccording to a given multi-step engagement strategy. Each delivery ofthe sequence may be configured differently in terms of the particularcontent components included in the delivery, a channel via which thecontent is delivered, a time at which the content is delivered (e.g.,both day of the week, time of day, days and since being targeted by acampaign), format of the delivered content, and so forth.

These differences in what, how, and when the content is delivered may becontrolled by delivery “aspects.” As used herein, a delivery “aspect”refers to a configurable characteristic of a content delivery,controlling what content is delivered to a client device as well as howand when the content is delivered. The described systems may control avariety of aspects of content delivery without departing from the spiritor scope of the techniques described herein.

The term “client device user” refers generally to a user of a clientcomputing device that is targeted by content deliveries which arecontrolled by a multi-step engagement strategy, e.g., a recipient of thecontent deliveries. As used herein, client device users may also referto users that interact with digital content in such a way as to becandidates for targeting by a multi-step engagement strategy, e.g., ifsuch interactions satisfy an entry condition of a campaign. Indeed,while a client device user is targeted by the content deliveries, thecontent is actually communicated to and ultimately presented via aclient device associated with the user.

In contrast, the term “engagement-system user” refers to a user of alearning-based engagement system as described above and below. Inaccordance with the techniques described herein, the engagement-systemuser may provide input to the engagement system via a computing deviceto specify content deliveries for user-created multi-step engagementstrategies, to cause the engagement system to automatically determinethe content deliveries for multi-step engagement strategies, viewmulti-step engagement strategies generated automatically or modified bythe engagement system, and so forth. By way of example, anengagement-system user may be a marketer for a company selling productsor services and client device users may be targeted by theengagement-system user as potential consumers of the products orservices offered for sale by the company.

As used herein, the term “entry condition” refers to an action, thatwhen performed by a client device user, causes the user to become atarget for receiving digital content in connection with a particularcampaign. Examples of actions that may be specified as entry conditionsinclude, but are not limited to, purchasing a ticket having a level thatis less than a “best” level (e.g., an economy ticket when first-classtickets are still available or general admission when premium seating isstill available), leaving a brick-and-mortar store without purchasing aproduct or service, leaving an online store without purchasing a productor service, adding a product or service to an online shopping cartwithout purchasing the added product or service, viewing anadvertisement, interacting with digital content corresponding to aproduct or service for sale, signing up to receive information from acompany, and so on.

As used herein, the term “exit condition” refers to an action, that whenperformed by a client device user, causes the user to no longer be atarget for receiving digital content in connection with a particularcampaign. Examples of actions that may be specified as exit conditionsinclude, but are not limited to, upgrading from a lower ticket level toa higher ticket level (e.g., upgrading to a first-class ticket orpremium seating), returning to a brick-and-mortar store to purchase aproduct or service, purchasing a product or service from an online store(e.g., associated with brick-and-mortar locations and/or limited toonline sales), purchasing a product or service previously added to anonline shopping cart but not previously purchased, purchasing a productor service corresponding to a viewed advertisement, purchasing a productor service corresponding to interacted-with digital content, and so on.

In the following discussion, an example environment is first describedthat may employ the techniques described herein. Example implementationdetails and procedures are then described which may be performed in theexample environment as well as other environments. Consequently,performance of the example procedures is not limited to the exampleenvironment and the example environment is not limited to performance ofthe example procedures.

Example Environment

FIG. 1 is an illustration of an environment 100 in an exampleimplementation that is operable to employ machine-learning basedmulti-step engagement strategy generation and visualization as describedherein. The illustrated environment 100 includes a service providersystem 102, computing device 104, and learning-based engagement system106 that are communicatively coupled, one to another, via a network 108.

Computing devices that are usable to implement the service providersystem 102, computing device 104, and learning-based engagement system106 may be configured in a variety of ways. A computing device, forinstance, may be configured as a desktop computer, a laptop computer, amobile device (e.g., assuming a handheld configuration such as a tabletor mobile phone), and so forth. Thus, the computing device may rangefrom full resource devices with substantial memory and processorresources (e.g., personal computers, game consoles) to a low-resourcedevice with limited memory and/or processing resources (e.g., mobiledevices). Additionally, a computing device may be representative of aplurality of different devices, such as multiple servers utilized by abusiness to perform operations “over the cloud” as further described inrelation to FIG. 7.

The service provider system 102 is illustrated as including a servicemanager module 110 that is representative of functionality to provideservices accessible via the network 108 that are usable to make productsor services available to consumers. The service manager module 110, forinstance, may expose a website or other functionality that is accessiblevia the network 108 by a communication module 112 of the computingdevice 104. The communication module 112, for instance, may beconfigured as a browser, a network-enabled application, and so on thatobtains data from the service provider system 102 via the network 108.This data is employed by the communication module 112 to enable a userof the computing device 104 to communicate with the service providersystem 102 to obtain information corresponding to the products orservices, e.g., web pages with dashboards and analytics tools when theservice provider system 102 is a marketing-campaign management service.

In order to personalize the information for client device users that aretargeted recipients of a campaign, the service provider system 102 mayemploy the learning-based engagement system 106. Although functionalityof the learning-based engagement system 106 is illustrated as separatefrom the service provider system 102, this functionality may also beincorporated as part of the service provider system 102, further dividedamong other entities, and so forth. The learning-based engagement system106 includes an engagement-strategy manager module 114 that isimplemented at least partially in hardware of a computing device tomodify personalized multi-step engagement strategies used to delivercontent of a campaign. The engagement-strategy manager module 114 isfurther implemented to generate and train generalized engagement model116 (GEM 116) and campaign specific model 118 (CSM 118). These modelsrepresent functionality to determine multi-segment engagement strategiesfor different defined segments of user data. Specifically, the GEM 116represents functionality to determine multi-segment engagementstrategies for the different defined segments based on data describinguser behavior over multiple historical campaigns. In contrast, the CSM118 represents functionality to determine multi-segment strategies forthe different defined segments based on data describing user behaviorover a “current” campaign—a campaign relative to which a currentengagement strategy is being deployed. To visualize multi-segmentengagement strategies generated or modified using the GEM 116 and theCSM 118, the engagement-strategy manager module 114 leveragesfunctionality of interpretation module 120. The interpretation module120 is configured as described in application Ser. No. 15/812,991, filedNov. 14, 2017 and titled “Rule Determination for Black-Box MachineLearning Models,” the entire disclosure of which is hereby incorporatedby reference.

Broadly speaking, the multi-segment engagement strategies determinedusing the functionality of the GEM 116 and the CSM 118 describe how todeliver content to particular segments of users, e.g., by specifying atleast a number of content deliveries and for each content delivery, adelivery channel, day of the week, and time of day. In one or moreimplementations, the engagement-strategy manager module 114 uses themulti-segment engagement strategies determined by the GEM 116 and theCSM 118 to deliver to client devices digital content 122, which isillustrated in storage 124, in an attempt to cause users of the clientdevices to perform a defined action. The defined action corresponds toan exit condition of the campaign; an objective of the campaign asdefined by an engagement-system user that interacts with the describedsystem via the computing device 104 to deploy a multi-segment engagementstrategy. Examples of such defined actions include, but are not limitedto, causing conversion with products or services of the service providersystem 102, upgrading from an economy class ticket to a premium classticket, subscribing to an email distribution list of the serviceprovider system 102, joining a loyalty program of the service providersystem 102, and so forth.

In accordance with the described techniques, the GEM 116 learnsmulti-segment engagement strategies for delivering the digital content122 in ways that optimize achievement of defined objectives based on oneor more machine learning techniques and user interaction data ofhistorical campaigns. The CSM 118 learns strategies for deliveringdigital content in ways that optimize achievement of a definedobjective. In particular, the CSM 118 does so as a particular campaignis underway based on one or more machine learning techniques and userinteraction data of the particular campaign. By way of example, the GEM116 and/or the CSM 118 may comprise a neural network trained based onone or more loss functions and back propagation to optimize delivery ofthe digital content 122 to client devices in a way that maximizeachievement of a defined campaign objective.

The digital content 122 may assume a variety of forms, such as images126, videos 128, text 130, and so forth. The learning-based engagementsystem 106 may deliver different images 126, videos 128, text 130, orcombinations thereof, to different client devices based on output of theGEM 116 and the CSM 118 that indicates which image 126, video 128, ortext 130 to deliver—the output generated based on data describingassociated client device users and machine learned “rules.” The digitalcontent 122, for instance, is provided to client devices as part of acampaign that is controlled by a personalized multi-step engagementstrategy. Data may then be generated based on provision of the digitalcontent 122 to describe which users received which items of the digitalcontent 122 (e.g., in connection with particular engagement strategies)as well as characteristics of the users. This generated data may beincluded in user data 132, for example.

The user data 132 may also assume a variety of forms without departingfrom the spirit or scope of the techniques described herein. Broadlyspeaking, the user data 132, which is illustrated as stored in storage134, describes characteristics of different client device users. Thisinformation is used to describe behaviors of a client device user withdelivered digital content via a respective client device and also totailor information provided to a client device based on particularcharacteristics of the associated user. This information also describesthe characteristics determined about client device users. To this end,the user data 132 is depicted with user profile 136, which representsdata describing an individual client device user, e.g., a userauthenticated to a client device.

The illustrated example depicts the user profile 136 having demographicinformation 138 (e.g., age, gender, and location), preferencesinformation 140 (e.g., preferred content delivery channels, contentdelivery restrictions, and products and services of interest), deliverylog 142 (e.g., records of the digital content 122 that has beendelivered to the user described by the user profile 136), and trackingdata 144 (e.g., data describing interactions of the client device userwith the digital content 122 delivered such as opens and clicks). Theuser data 132 and the user profiles 136 may be configured to include orhave access to different information without departing from the spiritor scope of the techniques described herein. By way of example, the userdata 132 may include the delivery log 142 describing the digital content122 delivered to multiple client devices (e.g., hundreds, thousands, andtens- or hundreds-of-thousands), rather than include the delivery log142 at a user-profile level describing merely the digital content 122delivered to a single user corresponding to the user profile 136. Theuser data 132 is further illustrated with ellipses to indicate that thestorage 134 is configured to store information for multiple users.

The engagement-strategy manager module 114 not only trains the GEM 116using historical user data 132, but also trains the CSM 118 duringdeployment of a campaign. Initially, such a campaign is largelycontrolled according to an input multi-step engagement strategy. Later,this campaign may be controlled according a multi-step engagementstrategy that is modified by the engagement-strategy manager module 114in real-time as client device users interact with delivered content (ordo not interact with it). The GEM 116 and the CSM 118 support advantagesin relation to massive record flows, as collecting the user data 132 forhundreds, thousands, and more users typically results in vast amounts ofdata. Indeed, as the GEM 116 and CSM 118 are trained using more of theuser data 132, these models are able to more accurately predict userinteractions with content in substantially real-time. This user data 132can include an amount of data that would be inefficient, to the point ofnear-impossibility, to simply process under ‘brute force’ with ageneral-purpose computer. Instead, the GEM 116 and the CSM 118effectively reduce the collected user data 132, e.g., to learned weightsof different nodes at different layers of a corresponding neuralnetwork.

The engagement-strategy manager module 114 can configure the GEM 116 andthe CSM 118 using any type of machine-learning technique to enableprediction of multi-step engagement strategies for optimizing a campaignexit condition. According to various implementations, such amachine-learning model uses supervised learning, unsupervised learning,or reinforcement learning. For example, the machine-learning model caninclude, but is not limited to, decision trees, support vector machines,linear regression, logistic regression, Bayesian networks, random forestlearning, dimensionality reduction algorithms, boosting algorithms,artificial neural networks (e.g., fully-connected neural networks, deepconvolutional neural networks, or recurrent neural networks), deeplearning, etc. In any case, the engagement-strategy manager module 114may use machine-learning techniques to continually train and update theGEM 116 and the CSM 118 (or, in other words, to update a trainedmachine-learning model) to accurately predict user behaviors andstrategies for optimizing a desired objective.

The interpretation module 120 represents functionality to determinerules for machine-learned models, such as the GEM 116 and the CSM 118.In one or more implementations, these rules are configured as if-thenstatements specifying data that a machine-learning model outputs givenparticular input data. Given data describing client device userstargeted by a respective campaign and their interactions with datadelivered in connection with the campaign, for instance, the GEM 116 andthe CSM 118 each output an indication of a multi-step engagementstrategy to use for further delivery of content. By way of example, theGEM 116 and the CSM 118 output data identifying one of a plurality ofstrategies, e.g., a first strategy, a second strategy, and so forth. Incontrast, the interpretation module 120 outputs human-readable rules asif-then statements such as ‘IF 10≤age<25 AND state=California THENPredict class_1’. Here, ‘class_1’ corresponds to a particular multi-stepengagement strategy that is known.

As described in more detail below, the engagement-strategy managermodule 114 generates visualizations indicative of the multi-stepengagement strategies produced by the GEM 116 and the CSM 118 based onthe rules output by the interpretation module 120. For instance, theengagement-strategy manager module 114 generates these visualizations toindicate user segments identified by the rules (e.g., users between 10and 25 and from California) and lists a sequence of communicationscorresponding to the particular multi-step engagement strategiesidentified by the rules e.g., the sequence of communications associatedwith ‘class_1’.

Having considered an example environment, consider now a discussion ofsome example details of the techniques for machine-learning basedmulti-step engagement strategy generation and visualization in a digitalmedium environment in accordance with one or more implementations.

ML-Based Multi-Step Engagement Strategy Generation and Visualization

FIG. 2 depicts an example implementation 200 of data describing amulti-step engagement strategy usable for delivering content of acampaign. In the illustrated example 200, multi-step engagement strategydata 202 includes entry condition 204, exit condition 206, contentdelivery manifest 208, delivery restrictions 210, and automaticmodification selection 212. It should be appreciated that the multi-stepengagement strategy data 202 is one example of data that can be used todescribe various aspects for implementing a multi-step engagementstrategy. Indeed, data may be configured in a variety of other ways todescribe various aspects for implementing a multi-step engagementstrategy without departing from the spirit or scope of the techniquesdescribed herein.

In relation to the illustrated example 200, though, the multi-stepengagement strategy data 202 is usable to implement multi-stepengagement strategies created by engagement-system users (e.g.,marketers) as well as those created by the engagement-strategy managermodule 114. The engagement-strategy manager module 114 can be leveragedto create an entire multi-step engagement strategy based on user inputdescribing solely the entry condition 204 and the exit condition—thoughinputs describing delivery restrictions may also be leveraged. Themulti-step engagement strategy data 202 is also usable to implementmulti-step engagement strategies modified by the engagement-strategymanager module 114 from an original version, such as from an originalstrategy created by an engagement-system user or created by theengagement-strategy manager module 114.

The entry condition 204 describes an action, that when performed by aclient device user, causes the user to become a target for receivingdigital content in connection with a particular campaign. Examples ofactions that may be described by the entry condition 204 include, butare not limited to, purchasing a ticket having a level that is less thana “best” level (e.g., an economy ticket when first-class tickets arestill available or general admission when premium seating is stillavailable), leaving a brick-and-mortar store without purchasing aproduct or service, leaving an online store without purchasing a productor service, adding a product or service to an online shopping cartwithout purchasing the added product or service, viewing anadvertisement, interacting with digital content corresponding to aproduct or service for sale, signing up to receive information from acompany, and so on. Clearly, the entry condition 204 may correspond todifferent actions relative to which delivery of digital content may beinitiated for a campaign in accordance with the described techniques.

An engagement-system user (e.g., marketer) that is creating a multi-stepengagement strategy via an engagement-strategy interface, or that issimply requesting via such an interface that a multi-step engagementstrategy be automatically created by the engagement-strategy managermodule 114, may specify the entry condition 204 in a variety of ways,such as by selecting an action via an interface instrumentality,entering text describing the action into an interface instrumentality,speaking the action to a voice-based interface, and so forth. Withrespect to the entry condition 204, once the described action isdetected to have occurred, the learning-based engagement system 106initiates delivery of digital content to a client device of a user thatperformed the described action and in accordance with the correspondingmulti-step engagement strategy.

In contrast, the exit condition 206 describes an action, that whenperformed by a client device user, causes the user to no longer be atarget for receiving digital content in connection with the particularcampaign. Examples of actions that may be described by the exitcondition 206 include, but are not limited to, upgrading from a lowerticket level to a higher ticket level (e.g., upgrading to a first-classticket or premium seating), returning to a brick-and-mortar store topurchase a product or service, purchasing a product or service from anonline store (e.g., associated with brick-and-mortar locations and/orlimited to online sales), purchasing a product or service previouslyadded to an online shopping cart but not previously purchased,purchasing a product or service corresponding to a viewed advertisement,purchasing a product or service corresponding to interacted-with digitalcontent, and so on. Clearly, the exit condition 206 may correspond todifferent actions relative to which delivery of digital content may beceased in connection with a campaign without departing from thedescribed techniques.

Like the entry condition 204, a user that is creating a multi-stepengagement strategy, or requesting that one be automatically created,may specify the exit condition 206 in a variety of ways via anengagement-strategy interface without departing from the spirit or scopeof the techniques described herein. With respect to the exit condition206, once the described action is detected to have occurred, thelearning-based engagement system 106 may cease delivering additionaldigital content to client devices of a user that performed the describedaction, even though the additional content is scheduled for deliveryaccording to the multi-step engagement strategy.

Broadly speaking, the content delivery manifest 208 describes a schedulefor delivering digital content to users in connection with a particularcampaign. In other words, the content delivery manifest 208 describesmultiple different, ordered communications that are to be delivered to aclient device of a user in response to the client device user performingthe action described by the entry condition 204. In the illustratedexample 200, the content delivery manifest 208 includes delivery 214,which represents data describing a particular content delivery to auser. The content delivery manifest 208 is also depicted with ellipsesto indicate that the content delivery manifest 208 includes datadescribing at least one more delivery (and potentially several moredeliveries). In any case, the content delivery manifest 208 describes atleast two separate defined deliveries of content, such that eachdelivery is a “step” of the multi-step engagement strategy.

As illustrated, the delivery 214 includes delivery channel 216, content218, day of week 220, and time of day 222. It should be appreciated thatthat the delivery 214 may include different information to controlcontent delivery without departing from the spirit or scope of thetechniques described herein. In scenarios where an engagement-systemuser creates a multi-step engagement strategy, the user may provideinput via an engagement-strategy interface specifying these aspects foreach delivery of the respective multi-step engagement strategy. For afirst delivery, for instance, the engagement-system user may provideinput via the interface to specify a channel via which the firstdelivery is to be delivered, particular content that is to be deliveredas the first delivery, a day of the week the described content is to bedelivered, and a time of day the described content is to be delivered.In such scenarios, the engagement-system user also provides input viathe engagement-strategy interface to specify these aspects for at leasta second delivery, and potentially more deliveries.

In scenarios where the engagement-strategy manager module 114automatically creates a multi-step engagement strategy, however, theengagement-strategy manager module 114 determines a number of deliveriesas well as the noted aspects for each delivery, e.g., using the GEM 116and/or the CSM 118. Once determined, the engagement-strategy managermodule 114 automatically generates data corresponding to each of thedetermined deliveries, e.g., describing the respective determinedaspects. The engagement-strategy manager module 114 is also configuredto aggregate the data describing the determined deliveries to form thecontent delivery manifest 208.

With respect to the particular aspects that control serving the delivery214, consider first the delivery channel 216. Broadly speaking, thedelivery channel 216 identifies one of a plurality of different deliverychannels via which the content 218 is to be delivered. Examples ofdifferent delivery channels include email, text message (e.g., shortmessage service (SMS)), social networking service notification (e.g., apost to appear in a user's feed of a social networking site), a mobilepush notification (e.g., from a mobile application), a voicemail, avideo in streaming video feed, audio in a streaming audio feed, dynamicwebpage advertising spots, and so forth. Certainly, the delivery channel216 may specify different channels via which the content 218 can bedelivered without departing from the spirit or scope of the techniquesdescribed herein.

The content 218 represents the data that is to be delivered to a user inconnection with the delivery 214, e.g., to convey a message such as“receive a 20% discount” or “loyalty points expiring.” The content 218may include the actual data to be delivered, and correspond tocombinations of the digital content 122. In addition or alternately, thecontent 218 may indicate a storage location where the data to bedelivered is accessible, e.g., the storage 124. By way of example, thecontent 218 comprises one or more digital images (e.g., included in anemail or push notification), text (e.g., included as part of a textmessage, email, or push notification), video, audio, graphics, variouscombinations of such content components, and so on. The content 218 maycorrespond to a variety of types of digital content arranged indifferent ways to convey various desired messages in accordance with thedescribed techniques.

The day of the week 220 and the time of day 222 describe “when” thecontent 218 is to be delivered via the delivery channel 216. The day ofthe week 220 represents data identifying one of Monday, Tuesday,Wednesday, Thursday, Friday, Saturday, or Sunday, and the time of day222 identifies a time of an identified day at which the content 218 isto be delivered, e.g., 12:00 PM PT. The day of the week 220 and the timeof day 222 are simply examples of data that can be used to describe whenthe delivery 214 is to be performed. In implementation, the delivery 214may include other data to describe “when” to deliver the content 218,such as a date attribute specifying a month and year of the delivery214. Alternately or in addition, the delivery 214 may simply includedata corresponding to a timestamp—a single data item indicative of year,month, day, and time of day—for the delivery 214. Nonetheless, the dayof the week 220 and the time of day 222 may be useful attributes forimplementation of the delivery restrictions 210.

In general, the delivery restrictions 210 describe restrictions tocontent delivery that an engagement-system user indicates are not tooccur in connection with the respective multi-step engagement strategy.By way of example, one delivery restriction may be that deliveries ofcontent for the respective campaign are not to occur on Saturdays orSundays; the deliveries are restricted to weekdays. To this end, the dayof the week 220 attribute may be useful in indicating an allowabledelivery day. Another example delivery restriction may be thatdeliveries of content for the respective campaign are not to occur after9:00 PM; the deliveries are restricted in a given day to 12:00 AM to9:00 PM. Another example may restrict channels via which content is tobe delivered in connection with the respective campaign, e.g., thedelivery restrictions 210 may describe that emails and SMS text messagescan be used but not other forms of communication. It is to beappreciated that the delivery restrictions 210 may indicate differentrestrictions in accordance with the described techniques.

The engagement-strategy manager module 114 leverages these deliveryrestrictions 210 in scenarios where it generates a multi-step engagementstrategy. The engagement-strategy manager module 114 also leveragesthese delivery restrictions in scenarios where an engagement-system usercreates a multi-step engagement strategy via an engagement interface andselects to allow the engagement-strategy manager module 114 to modifythe strategy throughout deployment. Consequently, the deliveryrestrictions 210 limit aspects of the deliveries the engagement-strategymanager module 114 is allowed to configure and ultimately communicate.In an example where the delivery restrictions 210 indicate thatdeliveries cannot be made on Sundays, for instance, theengagement-strategy manager module 114 may generate an SMS text messageor email message for a user and select a day of the week for deliveringthe generated SMS text other than Sunday. In other words, the deliveryrestrictions 210 prohibit the engagement-strategy manager module 114from delivering messages to the user on Sundays. Such deliveryrestrictions may also be specific to a particular user (as described bythe preferences information 140) or a segment of users, as determined bythe GEM 116 and the CSM 118. The delivery restrictions 210 may also beused to populate an engagement-strategy interface for assisting anengagement-system user in creating an input multi-step engagementstrategy. In a similar fashion as noted above, an engagement-system user(e.g., marketer) may indicate via the user interface that deliveriescannot be made to recipient client device users on Sundays. Based onthis, the engagement-strategy manager module 114 may populate interfaceinstrumentalities that assist user-creation of individual deliveries sothat Sundays are not selectable by the engagement-system user for adelivery 214.

The automatic modification selection 212 indicates whether theengagement-strategy manager module 114 is allowed to modify a multi-stepengagement strategy as content is delivered in accordance therewith. Inone or more implementations, the automatic modification selection 212may be a binary indicator, such that ‘1’ directs the engagement-strategymanager module 114 to modify the strategy and ‘0’ directs theengagement-strategy manager module 114 not to modify the strategy. Thisindication may be represented in other ways without departing from thespirit or scope of the techniques described herein. In any case, anengagement-system user may provide a selection via a user interfaceindicating that a multi-step engagement strategy is to be modified (ornot) by the engagement-strategy manager module 114. Accordingly, theautomatic modification selection 212 is indicative of this selection,which is described below in relation to FIG. 4. In accordance with thedescribed techniques, however, consider first FIG. 3.

FIG. 3 depicts an example 300 of a user interface via which a userprovides input for entry and exit conditions used by the learning-basedengagement system to generate and present a multi-step engagementstrategy.

The illustrated example 300 includes an engagement-strategy interface302, which is depicted as a displayable dialog box, though otherconfigurations are contemplated in the spirit and scope of thetechniques described herein. In the illustrated example 300, theengagement-strategy interface 302 is depicted in a first stage and asecond stage. These first and second stages are represented by thedepicted circles numbered ‘1’ and ‘2’, respectively. As illustrated, theengagement-strategy interface 302 includes an entry-conditioninstrumentality 304, an exit-condition instrumentality 306, and a submitinstrumentality 308.

The entry-condition instrumentality 304 and the exit-conditioninstrumentality 306 represent functionality to enable a user to specifyactions for the entry condition 204 and the exit condition 206,respectively. The engagement-strategy manager module 114 supportsfunctionality to configure the entry condition 204 and the exitcondition 206 to indicate the specified actions. Though illustrated asdropdowns via which actions can be selected for use as the entry andexit conditions, the entry-condition instrumentality 304 and theexit-condition instrumentality 306 may be configured in different waysto enable a user to specify actions for the entry and exit conditions,such as configured as text entry fields, voice prompts output via aspeaker in connection with a voice-based interface, and so on.

The submit instrumentality 308 represents functionality of theengagement-strategy interface to enable a user to submit the entry andexit conditions 204, 206 to the engagement-strategy manager module 114.Selection of the submit instrumentality 308 is thus effective tocommunicate a request to the engagement-strategy manager module 114 togenerate a multi-step engagement strategy based on the entry and exitconditions 204, 206 entered via the engagement-strategy interface 302.In contrast to the scenario represented by FIG. 4, the illustratedexample 300 represents a scenario in which a user does not provide inputto define or schedule a sequence of deliveries. Instead, the illustratedexample 300 represents a scenario in which the engagement-strategymanager module 114 uses functionality of the GEM 116 to generate amulti-step engagement strategy “from scratch”. As noted above and below,the GEM 116 is trained using data describing user interactions withcontent delivered in connection with historical campaigns. Theengagement-strategy manager module 114 also includes or has access tofunctionality for generating visualizations of multi-step engagementstrategies produced using the GEM 116 and the CSM 118.

Visualized deliveries 310 represent a sequence of deliveries determinedby the engagement-strategy manager module 114 using the GEM 116. Inparticular, the visualized deliveries 310 represent a sequencedetermined by the GEM 116 based submission of the entry and exitconditions 204, 206 as specified via the entry-condition instrumentality304 and the exit-condition instrumentality 306. As noted above, theengagement-strategy manager module 114 leverages functionality of theinterpretation module 120 to generate such visualizations. Theinterpretation module 120 determines user segments that the GEM 116associates with particular multi-step engagement strategies, asdescribed in application Ser. No. 15/812,991. In the illustrated example300, for instance, the interpretation module 120 determines that the GEM116 associates users from 10 to 25 and from California with amulti-segment engagement strategy having deliveries including ‘10%Disc.’, ‘Loyalty Pts. Exp.’, ‘15% Disc.’, ‘Free Meal’, ‘Delivery 5’, and‘Delivery 6’.

The engagement-strategy interface 302 also includes current user-segmentindication 312 along with next and previous segment instrumentalities314, 316. The next and previous segment instrumentalities 314, 316enable a user to view the delivery sequences determined by theengagement-strategy manager module 114 for at least two other segmentsof users. Although depicted with the visualized deliveries 310, thecurrent user-segment indication 312, the next segment instrumentality314, and the previous segment instrumentality 316, the described systemsmay generate visualizations of multi-segment engagement strategies thatare configured in myriad other ways without departing from the spirit orscope of the described techniques. The interpretation module 120 mayalso be leveraged to visualize multi-step engagement strategies that theengagement-strategy manager module 114 has modified from a user-createdor from a system-created engagement strategy. This provides transparencyto a user indicating how the engagement-strategy manager module 114 iscarrying out content delivery for a campaign. In the context ofmodifying user-created engagement strategies, consider now FIG. 4.

FIG. 4 depicts an example 400 of a user interface via which a user cancreate a multi-step engagement strategy and select to enable alearning-based engagement system to generate a modified multi-stepengagement strategy using machine learning.

The illustrated example 400 includes an engagement-strategy interface402, which is depicted as a displayable dialog box, though otherconfigurations are contemplated in the spirit and scope of thetechniques described herein. As illustrated, the engagement-strategyinterface 402 includes an entry-condition instrumentality 404, anexit-condition instrumentality 406, a delivery-defining portion 408, anautomatic-modification instrumentality 410, and a submit instrumentality412.

The entry-condition instrumentality 404 represents functionality toenable a user to specify an action for the entry condition 204. Theengagement-strategy manager module 114 configures the entry condition204 to indicate the specified action. The entry-conditioninstrumentality 404 is illustrated as a drop-down via which an actioncan be selected for use as the entry condition 204. Nevertheless, anentry-condition instrumentality may be configured in different ways toenable a user to specify an action for the entry condition 204.

Similarly, the exit-condition instrumentality 406 representsfunctionality to enable a user to specify an action for the exitcondition 206. The engagement-strategy manager module 114 configures theexit condition 206 to indicate the specified action. The exit-conditioninstrumentality 406 is illustrated as a drop-down via which an actioncan be selected for use as the exit condition 206. Nevertheless, anexit-condition instrumentality may also be configured in different waysto enable a user to specify an action for the exit condition 206.

The delivery-defining portion 408 represents functionality to enable auser to define aspects of a delivery—to provide input for creating thedeliveries 214 of the content delivery manifest 208. By way of example,the delivery-defining portion 408 includes functionality (or access tofunctionality) that enables a user to provide input for defining orotherwise identifying the delivery channel 216, the content 218, the dayof the week 220, and the time of day 222. In one or moreimplementations, the delivery-defining portion 408 includesfunctionality that enables a user to define other aspects of a delivery,e.g., depending on a particular configuration of data corresponding tothe delivery.

In the illustrated example 400, the delivery-defining portion 408 isdepicted displaying at least some aspects of already-instantiateddeliveries. Here, the already-instantiated deliveries include ‘10%Disc.’, ‘Loyalty Pts. Exp.’, ‘15% Disc.’, ‘Free Meal’, ‘Delivery 5’, and‘Delivery 6’. Each of these represent a separate delivery 214 of acontent delivery manifest 208. Although not specifically shown, thedelivery-defining portion 408 also includes functionality or access tofunctionality to enable a user to modify aspects of a delivery, such asto change a channel, day, time, and so forth. The delivery-definingportion 408 also includes add new instrumentality 414, which representsfunctionality to add an additional delivery 214 to the content deliverymanifest 208.

Broadly speaking, the deliveries depicted in the delivery-definingportion 408 represent user-defined deliveries for a single campaign.Collectively, these defined deliveries are a multi-step engagementstrategy for the campaign, leveraged after occurrence of a defined entrycondition and carried out until occurrence of a defined exit condition.In addition or alternately, the delivery-defining portion 408 mayinclude deliveries for multiple different campaigns and/or multipledifferent user segments, e.g., males from Texas and females fromCalifornia. In such implementations, the delivery-defining portion 408may also display an indication of the respective campaign and/or usersegment. Such arrangements enable a user to easily create similardeliveries for different campaigns and/or segments, such as by copying adelivery already created for a first campaign and then simply providinginput to change an identifier of the respective campaign from the firstcampaign to a second campaign. In any case, the engagement-strategyinterface 402 can be configured with a variety of functionality toenable users to create deliveries for a campaign controlled by amulti-step engagement strategy without departing from the spirit orscope of the described techniques.

The automatic-modification instrumentality 410 represents functionalityof the engagement-strategy interface 402 to enable a user to specifythat a multi-step engagement strategy is to be modified duringdeployment by the engagement-strategy manager module 114. In theillustrated example 400, the automatic-modification instrumentality 410is depicted as a check box. Thus, when user input is received to “check”the check box, the engagement-strategy manager module 114 configures theautomatic modification selection 212 to indicate that the respectivemulti-step engagement strategy is to be dynamically modified duringdeployment. It follows that when the check box is not “checked,” theengagement-strategy manager module 114 configures the automaticmodification selection 212 to indicate that the respective multi-stepengagement strategy is not to be modified during deployment. Theautomatic-modification instrumentality 410 may be configured indifferent ways to enable a user to specify that a multi-step engagementstrategy is to be modified during deployment by the engagement-strategymanager module 114 without departing from the spirit or scope of thedescribed techniques.

The submit instrumentality 412 represents functionality of theengagement-strategy interface 402 to enable a user to deploy a definedmulti-step engagement strategy. Responsive to selection of the submitinstrumentality 412, for instance, the engagement-strategy managermodule 114 deploys the defined multi-step engagement strategy. When aselection is received indicating to modify the strategy, theengagement-strategy manager module 114 modifies the multi-stepengagement strategy during deployment. With regard to modification of amulti-step engagement strategy during deployment, consider FIG. 5.

FIG. 5 depicts an example implementation 500 in which a learning-basedengagement system of FIG. 1 generates a modified multi-step engagementstrategy using machine learning from an input multi-step engagementstrategy.

The illustrated example 500 includes from FIG. 1 the engagement-strategymanager module 114 of the learning-based engagement system 106. Asdepicted in the environment of FIG. 1, the engagement-strategy managermodule 114 also includes the generalized engagement model 116 (GEM 116)and the campaign specific model 118 (CSM 118). In this example 500, theengagement-strategy manager module 114 also includes content deliverymodule 502 and strategy integration module 504. In implementation, theengagement-strategy manager module 114 may include or have access tomore or fewer modules and machine-learning models to carry out thedescribed functionality without departing from the spirit or scope ofthe techniques described herein. By way of example, the functionalityrepresented by the GEM 116, the CSM 118, and the strategy integrationmodule 504 may be combined into a single module or distributed amongmultiple additional modules in accordance with the described techniques.

Regardless of the implementation details, the engagement-strategymanager module 114 is depicted receiving input multi-step engagementstrategy 506 as input. The input multi-step engagement strategy 506 maybe created according to user input specifying details of contentdeliveries or created by the GEM 116 based on user input specifyingsimply entry and exit conditions of a campaign (content deliveryrestrictions may be specified also)—but not specifying details ofcontent deliveries. In particular, the content delivery module 502 isdepicted receiving the input multi-step engagement strategy 506 asinput. In accordance with the described techniques, the input multi-stepengagement strategy 506 corresponds to multi-step engagement strategydata 202 describing a multi-step engagement strategy that is created byan engagement-system user, e.g., via the engagement-strategy interface402. In other words, the input multi-step engagement strategy 506describes content deliveries where delivery aspects are definedaccording to user input. These contrast with content deliveries wherethe delivery aspects are defined according to determinations (e.g., ofchannel, day, and time) made by the engagement-strategy manager module114.

The content delivery module 502 represents functionality to delivercontent to recipient client device users 508 (recipients 508), at leastin part, according to the input multi-step engagement strategy 506. Inaccordance with the described techniques, the recipients 508 representclient device users that have performed the action indicated by theentry condition 204 of the input multi-step engagement strategy 506,e.g., users that have purchased an economy class ticket.

As noted just above, the content delivery module 502 delivers at leastsome content to the recipients 508 in accordance with the inputmulti-step engagement strategy 506. Such deliveries are represented byinput-directed deliveries 510. These input-directed deliveries 510 aredelivered according to the content delivery manifest 208 correspondingto the input multi-step engagement strategy 506. Thus, eachinput-directed delivery 510 corresponds to a delivery 214 and isdelivered according to the defined delivery channel 216, defined content218, defined day of the week 220, and defined time of day 222.

Although a user that created the input multi-step engagement strategy506 may define deliveries and schedule them based on his or her domainknowledge, the deliveries may not be delivered and/or scheduled in a waythat optimizes performance of the exit condition 206 among therecipients. In other words, the input multi-step engagement strategy 506may not be the optimal strategy for having the most recipients 508perform the action indicated by the exit condition 206. Instead, adifferent engagement strategy may cause more of the recipients 508 toperform the action indicated by the exit condition 206 than the inputmulti-step engagement strategy 506. When a selection is made toautomatically modify the input multi-step engagement strategy 506, theengagement-strategy manager module 114 is leveraged to find such adifferent strategy. In particular, the engagement-strategy managermodule 114 is leveraged to determine an optimized multi-step engagementstrategy, e.g., a strategy predicted to cause more of the recipients 508to perform the action defined by the exit condition 206 than any otherstrategy.

As part of determining an optimal multi-step engagement strategy, thecontent delivery module 502 also changes in a randomized fashion some ofthe delivery aspects defined by the user. These deliveries arerepresented by randomized deliveries 512. For the randomized deliveries512, the content delivery module 502 determines various aspects of theuser-scheduled deliveries to randomly modify. If the input multi-stepengagement strategy 506 describes delivering to a particular type ofuser a first email, then a second email, and then an SMS text, forinstance, the content delivery module 502 may randomly determine toinstead deliver the SMS text and then the first and second emails to theparticular user. Alternately or in addition, the content delivery module502 may randomly determine to deliver content at different times thandescribed by the input multi-step engagement strategy 506, or ondifferent days, or via different channels, or to add more deliveriesthan described, and so forth.

In any case, the content delivery module 502 serves the randomizeddeliveries 512 so that those deliveries deviate in some way from thedeliveries defined by the input multi-step engagement strategy 506. Thepurpose of the randomized deliveries 512 is to explore whether contentmay be delivered in other ways than described by the input multi-stepengagement strategy 506 that are more effective at causing the exitcondition 206 among the recipients 508. The randomized deliveries 512enable the engagement-strategy manager module 114 to observe how therecipients 508 interact with content delivered with randomly changedaspects relative to the input-directed deliveries 510. Initially, thecontent delivery module 502 delivers a first portion of the respectivecampaign's content with the input-directed deliveries 510 and a secondportion with the randomized deliveries 512. By way of example, thecontent delivery module 502 may initially deliver 80% of the campaign'scontent as the input-directed deliveries 510 and 20% as the randomizeddeliveries 512. The content delivery module 502 may deliver thecampaign's content in this way as part of an “exploration” phase. As theengagement-strategy manager module 114 determines and deploys an optimalmulti-step engagement strategy for the campaign, though, the amount ofrandomized deliveries 512 may be reduced. Further, the input-directeddeliveries 510 are replaced by system-directed deliveries (not shown),which are delivered according to the determined optimal engagementstrategy. Records of these content deliveries may be maintained in thedelivery log 142 data depicted in the illustrated environment 100.

Regardless of whether the content is delivered via an input-directeddelivery 510 or a randomized delivery 512, the recipients 508 interactwith those deliveries. By “interact,” it is meant that the recipients508 perform some action or series of actions in relation to theinput-directed deliveries 510 and the randomized deliveries 512. Suchinteractions may include, for instance, opening the content of adelivery (e.g., an email), deleting the delivery without opening thecontent, deleting the delivery after opening the content without otheractions, interacting with (e.g., selecting a portion of, viewing for aperiod of time, or listening for a period of time) the opened content,forwarding the delivery to another client device user, screen capturingthe content of the delivery, selecting a portion of the deliveredcontent to navigate to a web page or launch an application, performingthe action described by the exit condition 206, performing a negativeaction such as selecting an “unsubscribe” link, and so forth.

Interaction data 514 describes these actions performed by the recipients508 in relation to the input-directed deliveries 510 and the randomizeddeliveries 512 (and later system-directed deliveries). This interactiondata 514 may be collected by the client devices leveraged by therecipients 508 and communicated to the engagement-strategy managermodule 114 as the interactions occur. Techniques for collecting suchdata include insertion of tracking pixels, clickstream data, cookies,and so on. In accordance with the described techniques, this interactiondata 514 is one example of the tracking data 144. To this end, datadescribing the deliveries made by the content delivery module 502 iscaptured in the delivery log 142, and the interaction data 514describing interactions of the recipients 508 with those deliveries iscaptured in the tracking data 144.

In the illustrated example 500, the interaction data 514 and traininguser data 516 are depicted as input to the CSM 118 and the GEM 116,respectively. The interaction data 514 and the training user data 516both correspond to at least portions of the user data 132. Althoughdepicted receiving the interaction data 514, which may correspond to aportion of the delivery log 142 and the tracking data 144 of therecipients 508, other user data 132 associated with the recipients 508may be provided to the CSM 118, such as the demographic information 138and the preferences information 140. Broadly speaking, the training userdata 516 represents historical data describing client device users andtheir interactions with historical content delivery campaigns, e.g.,prior to deployment of the input multi-step engagement strategy 506. Incontrast, the interaction data 514 represents data collected during thecampaign corresponding to the input multi-step engagement strategy 506,which is increasingly controlled over its deployment by modifiedmulti-step engagement strategy 518.

As noted above, the GEM 116 represents functionality to generatemulti-step engagement strategies for different users (e.g., userscorresponding to different segments) based on historical user data.Broadly speaking, the engagement-strategy manager module 114 trains theGEM 116 with the training user data 516 of multiple historicalcampaigns. Additionally, the engagement-strategy manager module 114updates the GEM 116 as engagement strategies of campaigns are deployed.In so doing, the GEM 116 learns aspects of engagement strategies whichcause them to be effective at achieving particular objectives. The GEM116 also learns aspects of engagement strategies which cause them not tobe effective at achieving particular objectives. In this way, the GEM116 may be leveraged to predict information indicative of strategiesthat are effective across any campaign.

To this end, the training user data 516 includes the delivery logs 142and the tracking data 144 of multiple users for these multiplehistorical campaigns. The engagement-strategy manager module 114 trainsthe GEM 116 based on the content delivered according to the deliverylogs 142 and the user interactions with the delivered content asdescribed by the tracking data 144. The tracking data 144 indicatesinteractions with delivered content that resulted in achievement of anexit condition, e.g., “successful” interactions. The tracking data 144also indicates the interactions with the delivered content that did notresult in achievement of the exit condition, e.g., “unsuccessful”interactions. The engagement-strategy manager module 114 associatesdelivered content with interactions as described by the delivery logs142 and the tracking data 144, respectively. The engagement-strategymanager module 114 then converts these content-action pairs to data(e.g., feature vectors) that can be used to train a machine-learningmodel, such as neural network, random forest, or regression model. Theengagement-strategy manager module 114 uses these content-action pairsto train the GEM 116. As output, the GEM 116 provides a strategy fortargeting a recipient of a particular user segment given a campaignobjective.

In contrast to the GEM 116, the CSM 118 is built and deployed for asingle campaign. Thus, the CSM 118 receives data specific to clientdevice users that receive content delivered as part of the campaign andspecific to interactions of those client device users with the contentdelivered as part of the campaign. During deployment of this campaign,the engagement-strategy manager module 114 continues to update the CSM118 as the recipients 508 interact with the delivered content. However,the CSM 118 is limited in use to the particular campaign, such that fordifferent campaigns the engagement-strategy manager module 114 usesdifferent campaign specific models trained with interaction data of therespective campaign. For these different campaigns, however, theengagement-strategy manager module 114 uses the GEM 116. Regardless, aspart of continually training the CSM 118 during a respective campaign,the engagement-strategy manager module 114 also creates content-actionpaired data. This paired data is similar to the paired data describedabove in relation to the GEM 116, but in contrast is limited to datacorresponding to the respective campaign.

In accordance with the described techniques, the engagement-strategymanager module 114 may also determine segments for different users,e.g., based on the demographic information 138 and the preferencesinformation 140. The engagement-strategy manager module 114 uses thesedetermined segments for training the GEM 116 and the CSM 118 along withthe content-action paired data.

The strategy integration module 504 represents functionality to combinethe outputs of the GEM 116 and the CSM 118. For example, when the outputof these models is a vector from each indicative of a multi-stepengagement strategy for delivering content to a user segment, thestrategy integration module 504 combines these two vectors into a singlevector indicative of one multi-step engagement strategy. By combiningthe outputs of the GEM 116 and the CSM 118, the strategy integrationmodule 504 outputs the modified multi-step engagement strategy 518. Inone or more implementations, the modified multi-step engagement strategy518 controls content delivery to a single segment of the recipients 508,such as a segment corresponding to females between 20 and 24 fromCalifornia or a segment corresponding to males between 30 and 34 fromTexas.

In summary, the GEM 116 outputs a multi-step engagement strategy for agiven user segment based on being trained with the training user data516 from multiple historical campaigns. In one or more implementations,the CSM 118 may also output a multi-step engagement strategy for thegiven user segment. However, the CSM 118 may do so based on the outputof the GEM 116 and also based on training involving the interaction data514. The modified multi-step engagement strategy 518 is used by thecontent delivery module 502 to deliver content of a respective campaignto the recipients 508. Over a life of the campaign, the input multi-stepengagement strategy 506 is used increasingly less and the modifiedmulti-step engagement strategy 518 is used increasingly more effectiveto reduce a number of the input-directed deliveries 510 relative to thesystem-directed deliveries (delivered according to the modifiedmulti-step engagement strategy 518). Consider the followingimplementation example in accordance with the described techniques.

Initially, the content delivery module 502 leverages an epsilon-greedytraffic allocation strategy to determine an amount of deliveries toselect as the input-directed deliveries 510, an amount to select as therandomized deliveries 512, and an amount to select as system-directeddeliveries. In practice, this means that with a first probability ofepsilon (e.g., 0.2) a given recipient 508 will be served contentdetermined by the engagement-strategy manager module 114, e.g.,determined using the outputs of the GEM 116 and the CSM 118 rather thanbe served content in accordance with the input multi-step engagementstrategy 506. These deliveries correspond to the system-directeddeliveries. Further, with a second probability of one minus epsilon(e.g., 1-0.2), a given recipient 508 will be service content accordingto the input multi-step engagement strategy 506. These correspond to theinput-directed deliveries 510.

During the course of the campaign, the content delivery module 502serves the randomized deliveries 512 with a probability equal to beta.As discussed above, these randomized deliveries 512 allow theengagement-strategy manager module 114 to observe interactions withcontent deliveries that are different from those specified by a userthat created the input multi-step engagement strategy 506. The observedinteractions with the randomized deliveries 512 are captured in data asdescribed above—this allows a data set to be built describing theinteractions with the deliveries different from those specified by theuser.

Continuing with this implementation example, the engagement-strategymanager module 114 trains a neural network configured as the CSM 118. Inparticular, the engagement-strategy manager module 114 trains the CSM118 to receive a given user state as input and, based on this input,generate a prediction of a multi-step engagement strategy that maximizesfuture reward as output. In this implementation example, the data setused to train the CSM 118 comprises a set of “episodes,” where eachepisode comprises a sequence of actions taken by a recipient to whichcontent is delivered. Each of these episodes is also associated with areward. In this implementation example, the rewards are associated withthe episodes as follows though rewards may be associated with sequencesof actions for training differently in different implementations. Areward of positive one (+1) is associated with an episode if therespective user performs the action described by the exit condition 206at the end of the episode. A reward of zero (0) is associated with anepisode if the respective user does not perform the action described bythe exit condition 206 and also does not perform a negative action. Areward of negative one (−1)—a penalty—is associated with an episode ifthe respective user does not perform the exit-condition action, but doesperform a negative action, such as unsubscribing to an emaildistribution list, deleting an account with a service, and so forth. TheCSM 118 may be flexible insofar as using a different reward structuremay incentivize different types of optimization strategies.

The engagement-strategy manager module 114 uses these training episodesto train the CSM 118 to generate a prediction comprising a probabilityvector of actions, given a current state of a recipient 508 as input.The current state corresponds to characteristics of the recipient 508 asdescribed by the user data 132, e.g., location, products purchased on acorresponding website, demographic information, preferences information,delivered content, interactions therewith, and so forth.

In addition to training the CSM 118, the content delivery module 502serves a first portion of users content according to the output of theCSM 118 (e.g., according to the modified multi-step engagement strategy518) and a second portion of the users content according to the inputmulti-step engagement strategy 506. The portion served content accordingto the output of the CSM 118 is based on a rate at which the recipientsperform the action described by the exit condition 206, such that thecontent delivery module 502 serves more recipients 508 content accordingto the CSM 118's output for higher “conversion” rates. Accordingly, asthe CSM 118 learns user behavior patterns—based on observing userinteraction with the delivered content as described by the interactiondata 514—the first portion of users increases toward all the recipientsbeing served the content according to the CSM 118's output.

Finally, the randomness introduced by this greedy-epsilon process duringtraining allows the CSM 118 and thus the engagement-strategy managermodule 114 to capture changing user trends, e.g., in ways users interactwith delivered content. This is because the exploration of randomaspects of content delivery allows the engagement-strategy managermodule 114 to determine alternative strategies that are useful inpractice which a user creating the multi-step engagement strategy maynot have considered in its creation.

Having discussed example details of the techniques for machine-learningbased multi-step engagement strategy generation and visualization,consider now some example procedures to illustrate additional aspects ofthe techniques.

Example Procedures

This section describes example procedures for machine-learning basedmulti-step engagement strategy generation and visualization in one ormore implementations. Aspects of the procedures may be implemented inhardware, firmware, or software, or a combination thereof. Theprocedures are shown as a set of blocks that specify operationsperformed by one or more devices and are not necessarily limited to theorders shown for performing the operations by the respective blocks. Inat least some implementations the procedures are performed by a suitablyconfigured device, such as the learning-based engagement system 106 ofFIG. 1 that makes use of an engagement-strategy manager module 114,including use of its GEM 116 and CSM 118.

FIG. 6 depicts an example procedure 600 in which a multi-step engagementstrategy is created using machine learning and visualized for displayvia a user interface.

A machine-learning model is trained using tracked interactions of clientdevice users with content delivered in connection with historicalcampaigns (block 602). In accordance with the principles discussedherein, the machine-learning model is trained to generate predictions ofmulti-step engagement strategies for optimizing achievement of an exitcondition. By way of example, the engagement-strategy manager module 114trains the generalized engagement model 116 (GEM 116) using the deliverylogs 142 and the tracking data 144 of the user data 132. In one or moreimplementations, the engagement-strategy manager module 114 trains theGEM 116 in accordance with the training described in above-noted andincorporated application Ser. No. 15/812,991. By way of example, theengagement-strategy manager module 114 trains the GEM 116, in accordancewith the training described in application Ser. No. 15/812,991, topredict a class corresponding to one of a plurality of predictablemulti-step engagement strategies based on input data describing a userstate.

User input is received that defines an entry condition and the exitcondition for a new campaign (block 604). In accordance with theprinciples discussed herein, this new campaign is to be created fordelivering content to client device users targeted based on the entrycondition. By way of example, the computing device 104 presents theengagement-strategy interface 302, and receives input via theengagement-strategy interface 302 to define the entry condition 204 andthe exit condition 206 for a new campaign. The engagement-strategymanager module 114 receives the entry condition 204 and the exitcondition 206 from the computing device 104.

A multi-step engagement strategy is generated for delivering content inconnection with the new campaign using the trained machine-learningmodel based on the defined exit condition (block 606). By way ofexample, the engagement-strategy manager module 114 uses the GEM 116 togenerate a multi-step engagement strategy for the new campaign based onthe exit condition 206 defined at block 604.

A visualization of the multi-step engagement strategy is generated(block 608). In accordance with the principles discussed herein, thevisualization is generated for display via a user interface and includesindications of at least two content deliveries of the new campaign thatare controlled by the multi-step engagement strategy. By way of example,the engagement-strategy manager module 114 generates a visualization ofthe multi-step engagement strategy generated at block 606. Withreference to application Ser. No. 15/812,991, the engagement-strategymanager module 114 generates these visualizations, in part, byleveraging generated rules indicative of user segments and predictedsegment classifications, e.g., ‘IF 10≤age<25 AND state=California THENPredict class_1’. Here, ‘class_1’ corresponds to a particular multi-stepengagement strategy. In one or more implementations, this visualizationis configured in a similar manner to the visualized deliveries 310 ofthe engagement-strategy interface 302, insofar as it includesindications of at least two content deliveries of the new campaign thatare controlled by the multi-step engagement strategy generated at block606. In this scenario, the engagement-strategy interface 302 alsoincludes instrumentalities to navigate through the visualized deliveries310 for different users and/or user segments.

Certainly, the at least two content deliveries of this new campaign maybe visualized in other ways without departing from the spirit or scopeof the described techniques. The engagement-strategy manager module 114communicates the generated visualization to the computing device 104 foroutput, e.g., display. It is to be appreciated that theengagement-strategy manager module 114 is capable of generatingvisualizations of user-created multi-step engagement strategies,visualizations of multi-step engagement strategies that are created bythe engagement-strategy manager module 114 based on input of the entrycondition 204 and the exit condition 206, visualizations of multi-stepengagement strategies that the engagement-strategy manager module 114modifies from user-created multi-step engagement strategies, and soforth.

Having described example procedures in accordance with one or moreimplementations, consider now an example system and device that can beutilized to implement the various techniques described herein.

Example System and Device

FIG. 7 illustrates an example system generally at 700 that includes anexample computing device 702 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe engagement-strategy manager module 114 and the communication module112. The computing device 702 may be, for example, a server of a serviceprovider, a device associated with a client (e.g., a client device), anon-chip system, and/or any other suitable computing device or computingsystem.

The example computing device 702 as illustrated includes a processingsystem 704, one or more computer-readable media 706, and one or more I/Ointerfaces 708 that are communicatively coupled, one to another.Although not shown, the computing device 702 may further include asystem bus or other data and command transfer system that couples thevarious components, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 704 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 704 is illustrated as including hardware elements 710 that may beconfigured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 710 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 706 is illustrated as includingmemory/storage 712. The memory/storage 712 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 712 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 712 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 706 may be configured in a variety of other waysas further described below.

Input/output interface(s) 708 are representative of functionality toallow a user to enter commands and information to computing device 702,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 702 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 702. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 702, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 710 and computer-readablemedia 706 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 710. The computing device 702 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device702 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements710 of the processing system 704. The instructions and/or functions maybe executable/operable by one or more articles of manufacture (forexample, one or more computing devices 702 and/or processing systems704) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 702 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 714 via a platform 716 as describedbelow.

The cloud 714 includes and/or is representative of a platform 716 forresources 718. The platform 716 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 714. Theresources 718 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 702. Resources 718 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 716 may abstract resources and functions to connect thecomputing device 702 with other computing devices. The platform 716 mayalso serve to abstract scaling of resources to provide a correspondinglevel of scale to encountered demand for the resources 718 that areimplemented via the platform 716. Accordingly, in an interconnecteddevice embodiment, implementation of functionality described herein maybe distributed throughout the system 700. For example, the functionalitymay be implemented in part on the computing device 702 as well as viathe platform 716 that abstracts the functionality of the cloud 714.

Conclusion

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. In a digital medium environment to cause targetedclient device users to perform a desired action, a method implemented byat least one computing device, the method comprising: receiving arequest from an engagement-system user for a multi-step engagementstrategy used to control delivery of content associated with a campaign,the request limited to an entry condition and an exit condition for thecampaign, the entry condition limited to describing an action performedby a client device user that causes the client device user to betargeted by the campaign, and the exit condition describing anadditional action performed by the client device user that causes theclient device user to no longer be targeted by the campaign; training amachine learning model with data describing interactions of clientdevice users with content delivered in connection with at least onehistorical campaign; generating, using the machine-learning model andwithout further input from the engagement-system user, a prediction ofthe multi-step engagement strategy that optimizes performance of theexit condition across the client device users targeted by the campaignresponsive to satisfying the entry condition, the multi-step engagementstrategy describing a sequence of content deliveries to at least oneclient device user targeted by the campaign; and controlling thedelivery of content associated with the campaign, in part, by servingthe sequence of content deliveries to the at least one client deviceuser targeted by the campaign as described by the multi-step engagementstrategy.
 2. A method as described in claim 1, wherein the multi-stepengagement strategy describes aspects of the sequence of contentdeliveries, including at least one of: a number of the contentdeliveries in the sequence; a delivery channel for delivering each ofthe content deliveries in the sequence; a day of a week for deliveringeach of the content deliveries in the sequence; a time of day fordelivering each of the content deliveries in the sequence; or acombination of content components included in each of the contentdeliveries in the sequence.
 3. A method as described in claim 2, whereinthe delivery channel comprises one of a plurality of channels includingat least one of email, short message service (SMS) text message,mobile-device push notifications, social networking serviceinstrumentalities, video in a streaming video feed, audio in a streamingaudio feed, or dynamic webpage advertising spots.
 4. A method asdescribed in claim 1, wherein the entry condition is specified by theengagement-system user via a user interface.
 5. A method as described inclaim 1, wherein the exit condition is specified by theengagement-system user via a user interface.
 6. A method as described inclaim 1, further comprising generating a visualization of the multi-stepengagement strategy, the visualization including visual indications ofthe content deliveries in the sequence.
 7. A method as described inclaim 6, further comprising communicating the visualization for outputto a computing device of the engagement-system user associated with therequest.
 8. A method as described in claim 6, wherein the visualindications indicate delivery aspects of a respective content delivery.9. A method as described in claim 8, wherein the delivery aspectsinclude at least one of a delivery channel for delivering the respectivecontent delivery, a date for delivering the respective content delivery,a time of day for delivering the respective content delivery, or amessage of the respective content delivery.
 10. A method as described inclaim 1, wherein the entry condition and the exit condition arespecified by user input of the engagement-system user, and the userinput of the engagement-system user is limited to submission of theentry condition and the exit condition.
 11. A system comprising: anengagement-strategy manager module implemented at least partially inhardware of at least one computing device to receive a request from anengagement-system user for a multi-step engagement strategy thatcontrols delivery of content associated with a campaign, the requestlimited to an entry condition and an exit condition, the entry conditionlimited to describing an action performed by a client device user thatcauses the client device user to be targeted by the campaign, and theexit condition describing an additional action performed by the clientdevice user that causes the client device user to no longer be targetedby the campaign; and a machine-learning model implemented at leastpartially in the hardware of the at least one computing device togenerate, without further input from the engagement-system user, aprediction of the multi-step engagement strategy that optimizesperformance of the exit condition across client device users that aretargeted by the campaign based on satisfying the entry condition, themulti-step engagement strategy describing a sequence of contentdeliveries for at least one of the client device users targeted by thecampaign.
 12. A system as described in claim 11, wherein theengagement-strategy manager module is further configured to generatevisualizations of the multi-step engagement strategy, the visualizationsincluding visual indications of the content deliveries in the sequence.13. A system as described in claim 11, wherein the engagement-strategymanager module is further configured to communicate a generatedvisualization to a computing device of the engagement-system userassociated with the request.
 14. A system as described in claim 11,wherein the engagement-strategy manager module is further configured tocause delivery of the sequence of content deliveries to a respectiveclient device of the at least one client device user targeted by thecampaign.
 15. A system as described in claim 11, wherein the multi-stepengagement strategy further describes an additional sequence of contentdeliveries for at least one additional user of the client device userstargeted by the campaign.
 16. A system as described in claim 15, whereinthe engagement-strategy manager module is further configured to causedelivery of the additional sequence of content deliveries to arespective client device of the at least one additional user.
 17. Asystem as described in claim 11, wherein the engagement-strategy managermodule is further configured to receive an additional request for anadditional multi-step engagement strategy that controls delivery ofcontent associated with an additional campaign, the additional requestlimited to a respective entry condition, a respective exit condition,and at least one campaign delivery restriction that restricts thedelivery of content associated with the additional campaign.
 18. Asystem as described in claim 17, wherein the engagement-strategy managermodule is further configured to generate the additional multi-stepengagement strategy to describe only sequences of content deliveriesthat are not restricted by the at least one campaign deliveryrestriction.
 19. In a digital medium environment to inform a user of anengagement system how the engagement system controls delivery of contentassociated with a campaign, a method implemented by at least onecomputing device, the method comprising: receiving, via a user interfacepresented for an engagement-system user, input to request generation ofa multi-step engagement strategy for controlling delivery of contentassociated with the campaign, the input limited to submission of anentry condition and an exit condition, the entry condition limited todescribing an action performed by a client device user that causes theclient device user to be targeted by the campaign, and the exitcondition describing an additional action performed by the client deviceuser that causes the client device user to no longer be targeted by thecampaign; communicating the request to the engagement system; receiving,without further input from the engagement-system user, a visualizationof the multi-step engagement strategy generated by the engagementsystem, the multi-step engagement strategy describing a sequence ofcontent deliveries to at least one client device user targeted by thecampaign and determined using a machine learning model, thevisualization including visual indications corresponding to the sequenceof content deliveries described by the multi-step engagement strategy;and presenting, via the user interface, the visualization.
 20. A methodas described in claim 19, wherein the visualization further includes auser segment indication that indicates a user segment determined for theat least one client device user from a plurality of user segments by theengagement system.